Research On Distributed Passive Sensor Location Based On TDOA And FDOA Measurements

The passive localization is important for the regional situation awareness and target information acquisition.It plays an important role in obtaining the military intelligence of the battle.The conventional centralized localization system based on time difference of arrival(TDOA)and frequency difference of arrival(FDOA)has been a mature technology.However,in practical application,there are several shortcomings including high complexity for system,system clumsiness and weak survival ability,which is difficult to adapt to the new requirements of passive location,such as small support platform and strong survivability.Under this condition,the distributed localization system based on TDOA and FDOA has been the focus of research and development trend.Because,the sensor paring strategy can reduce the difficulty of time and frequency synchronization and improve the survivability of the system.In this paper,we aim at the low bias and high accuracy of the parameter estimation and accurate source location of distributed strategy,which can provide support for the theory and application of distributed localization based on TDOA and FDOA.The main work and achievements of this paper are summarized as following:1.Considering the traditional parameters estimation methods which exhibit the obvious bias,high variance and the estimation performance is limited by the sampling interval and the reciprocal of sampling time,a novel parameters estimation interpolation method based on second-order Cone Programming(SOCP)with sub-sample accuracy is presented in this paper.The proposed method chooses the discrete samples from pattern matching function results.Then the convex optimization models based on "soft" and "hard" constraints are constructed by using generalized extended approximation(GEA)method.Finally,the accurate estimation results can be obtained by the interpolation curve which is solved by SOCP.Simulation results indicate that the proposed method is effective and outperforms the other interpolation algorithms regarding the estimation accuracy,and effectively reduces the loss of information in the parameter estimation.2.Considering the traditional centralized localization methods cannot be directly used in distributed localization and the nonlinear nature of the source localization problem,which creates bias to a location estimate,a bias compensation algorithm based on maximum likelihood estimation(MLE)for distributed localization using TDOA and FDOA is presented in this paper.The proposed method firstly uses the methodology of number-shape combination to obtain a proper initial value of source position and velocity.Then,the coarse location estimation is obtained by MLE.Finally,more accurate estimation result is achieved by subtracting theoretical bias,which is approximated by the actual bias using the estimated source location and noisy data measurement.Simulations indicate that the proposed method can obviously reduce the bias of coarse estimation result,which can effectively improve the estimation accuracy.3.Considering the special scenario that the receivers cannot precisely obtain their position coordinates,the localization accuracies of the conventional methods will be seriously affected.Hence,a bias analysis based on MLE of source localization for distributed passive sensor using TDOA and FDOA measurements with receiver location uncertainties is proposed in this paper.This method adds the receiver location errors on the basis of the original model,and re-derived the theoretical bias of MLE and the CRLB of the distributed passive sensors localization.Simulation results show that the proposed method is less biased and outperforms the other localization methods in terms of estimation accuracy.